Fairness of machine learning models in healthcare has drawn increasing attention from clinicians, researchers, and even at the highest level of government. On the other hand, the importance of developing and deploying interpretable or explainable models has been demonstrated, and is essential to increasing the trustworthiness and likelihood of adoption of these models. The objective of this study was to develop and implement a framework for addressing both these issues - fairness and explainability. We propose an explainable fairness framework, first developing a model with optimized performance, and then using an in-processing approach to mitigate model biases relative to the sensitive attributes of race and sex. We then explore and visualize explanations of the model changes that lead to the fairness enhancement process through exploring the changes in importance of features. Our resulting-fairness enhanced models retain high sensitivity with improved fairness and explanations of the fairness-enhancement that may provide helpful insights for healthcare providers to guide clinical decision-making and resource allocation.

翻译：机器学习模型在医疗领域的公平性日益受到临床医生、研究人员乃至政府最高层级的关注。另一方面，开发并部署可解释或可理解的模型的重要性已得到证实，这对于增强模型的信任度和应用可行性至关重要。本研究旨在开发并实施一个同时解决公平性与可解释性问题的框架。我们提出一个可解释公平框架，首先构建具有优化性能的模型，随后采用进程内处理方法减轻模型在种族和性别敏感属性上的偏差。通过分析特征重要性的变化，我们探索并可视化导致公平性增强过程的模型变化解释。经公平性优化后的模型在保持高灵敏度的同时，提升了公平性及公平性增强机制的解释性，这可为医疗工作者提供有价值的洞见，以指导临床决策和资源分配。